Movement timestamping and analytics

ABSTRACT

In selected embodiments, a computer-implemented method for analyzing customer movement in a retail environment includes capturing an image of an individual at multiple locations within a retail environment and tracking the elapsed time between the individual&#39;s appearance at the various locations. For areas of the store at increased risk for shoplifting, the elapsed times may be compared to predetermined upper and/or lower elapsed time thresholds or windows to determine whether an individual is likely to commit a crime in the retail environment. The thresholds or window may be empirically determined based on analysis of historical security video footage and security incident records.

BACKGROUND

Video systems are commonly used to monitor commercial or retaillocations such as stores, warehouses, athletic facilities, etc. Inparticular, video systems are be used to monitor different locationswithin the interiors of such facilities, including high traffic aisles,security sensitive areas, and the like. Such systems are also used tomonitor locations near the exteriors of facilities, such as parkinglots, loading docks, external doors, etc.

Video monitoring is performed for one or more reasons. For example,video monitoring in a jewelry store is used primarily for storesecurity, and helps prevent theft and identify perpetrators, and is alsouseful in recovering merchandise after a theft has occurred. Indifferent situations where theft prevention is of less concern videomonitoring might be used primarily for safety purposes and to monitorfor overflow capacity of a particular area.

Videos and images are stored for later retrieval and viewing. Forexample, surveillance video is archived in a device that retains andprotects the video for a period of time. Archive systems protect videosand images from loss due to tampering, accidents, and equipment failure.Some systems have backup service, for example, through an externalprovider or using redundant internal systems.

Archived videos and images are selectively retrieved for analysis. Forexample, video of a roadway intersection is later retrieved to view anautomobile collision for purposes of reconstructing the accident. Somearchived videos and images are used in legal proceedings. For example,surveillance video of a convenience store checkout is often used asevidence against an accused thief or robber.

SUMMARY

In selected embodiments, a computer-implemented method for analyzingcustomer movement in a retail environment includes capturing an image ofan individual at multiple locations within a retail environment andtracking the elapsed time between the individual's appearance at thevarious locations. For areas of the store at increased risk forshoplifting, the elapsed times may be compared to predetermined upperand/or lower elapsed time thresholds or windows to determine whether anindividual is likely to commit a crime in the retail environment. Thethresholds or window may be empirically determined based on analysis ofhistorical security video footage and security incident records.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

This document describes these and other aspects in detail with referenceto the following drawings.

FIG. 1 is a conceptual diagram of a representative system for analyzinga person's movement.

FIG. 2 is an illustration of an example time-stamping of a person'smovement.

FIGS. 3A and 3B are a timeline diagram of example interactions amongvarious components of an example system for analyzing a person'smovement.

FIG. 4 is a flowchart of a representative process for analyzing aperson's movement.

FIG. 5 illustrates an example of a person's movement among severallocations.

FIG. 6 is a schematic diagram of an example of a generic computersystem.

Like reference symbols in various drawing indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE IMPLEMENTATIONS

Some implementations provide an at least partly automated solution todetect behavior indicative of prohibited activities such as theft,fraud, or other crimes. For example, some activities are prohibited bylaw (e.g., theft, fraud, and other crimes), or by contract (e.g., aperson agrees to refrain from the activity), or by policy (e.g., aproperty owner announces rules for those on the premises). For example,the owner of a retail establishment wishes to prevent customers or othervisitors, or retail employees, from engaging in prohibited activities.Doing so helps provide a comfortable shopping environment for customersand a safe workplace for employees and reduces financial loss for theretailer.

In some implementations, a time-stamping security video system observesmultiple locations of a retail environment. Attributes of personsobserved within each of the locations (e.g., a person's size, shape,clothing colors) are stored along with timestamps and as people moveamong multiple locations, a collection of time-indexed records iscreated. One or more identifying characteristics are then determined foreach person in the time-indexed records using image analysis and, basedon this the person, are then identified in one or more additionallocation. That is, the determination is made that the same person occursin the image records for two or more monitored locations. Thiscollection of the time stamped records of locations where the person hasbeen identified is then used to determine the durations of time theperson has taken to move between various locations.

Predetermined time criteria are defined for one or more pairs oflocations and, if the person's movement meets one or more of thesecriteria, a security notification is generated. For example, if a personis observed at a store entrance and is then spotted an unusually shorttime later at a high-value product display (e.g., product that arelikely targets of shoplifters), the system notifies a store associate orsecurity worker to observe or attend to the person more closely.

FIG. 1 is a conceptual diagram of a representative system 100 foranalyzing a person's movement. The system 100 includes at security videosystem 105. The security video system 105 is part of a collection of oneor more image capture devices and computer-based processors (e.g.,digital cameras, digital video recorders, and video digitizers). Thesecurity video system 105 captures and digitizes images of one or morelocations 110 as electronic image data files. The security video system105 associates captured images with time stamps.

The security video system 105 transmits the captured image to a videoanalytics system 115 over a communications network 120 (e.g., a localarea network, a cellular network, or the Internet). The video analyticssystem 115 is a computer-based system that recognizes characters in thecaptured images. For example, the video analytics system 115 uses imagerecognition technology to detect human beings in the image and identifyone or more parameters that characterize attributes of the detectedcharacter. Example processes for identifying characterizing parametersare discussed in connection with FIGS. 3A, 3B, and 4.

The video analytics system 115 characterizes attributes of the personusing one or more parameters. The attributes are then used to uniquelydistinguish a person from each of multiple persons visible in the sameor another image. The security video system 105 associates thoseattributes with the time stamp and with the location where the image wascaptured. In some implementations, one or more identifying parametersare used, such as color, size, shape, gender, clothing element, facialfeature, or other features of the person's body, appearance, orclothing. For example, the video security system identifies that aparticular person has a beard and wears a blue jacket and tan pants. Insome implementations, the video analytics system 115 is designed to alsorecognize items carried or transported by a person (e.g., a walkingcane) but to ignore or filter out objects that occur too frequently todistinguish persons from each other. For example, a system implementedin a retail establishment is trained to not register shopping carts ascharacteristic attributes.

The video analytics system 115 transmits the parameters, locationinformation, and time stamp to an archive system 135. The archive system135 is a server computer that includes a collection of storage devices(e.g., hard drives, optical drives, tape drives, a RAID array) thatsecurely stores a person's movement information, backs it up, andsubsequently retrieves and serves the person's movement information todisplay devices when requested. The archive system 135 allows onlyauthorized users to retrieve the person's stored movement information bycomparing a requesting user's identity with an access control list torestrict the distribution of the person's stored movement information toonly authorized users and/or authorized display devices. The archivesystem 135 optionally includes a web server that provides access to theperson's stored movement information through a web browser.

The video analytics system 115 analyzes the person's stored movementinformation to determine if the same person has appeared in more thanone location. When the person is known to have been recognized in morethan one location (e.g., the person has moved between at least twolocations in a store), then the time stamps associated with therecognitions are used to determine the amount of time the person hastaken to move between the locations.

A computer 140 and a handheld device 145 (e.g., cell phone, PDA)requests and displays analyzed movement information to one or moreusers. The computer 140 sends a request over the network 120 to thevideo analytics system 115 to request analyzed movement information fromthe video analytics system 115. The handheld device 145 communicateswith the network 120 over a wireless communications link 150 (e.g.,WiFi, cellular, Bluetooth, infrared) to request analyzed movementinformation from the video analytics system 115.

FIG. 2 illustrates an example of time stamping a person's movement. Inthe illustrated example, a video surveillance system 200 includes avideo surveillance camera 210 which captures images of a location suchas an entrance door 220. In some implementations, the video analyticssystem 230 corresponds to the video analytics system 115 of FIG. 1. Whena person enters the door 220, the person's presence is recognized by thevideo analytics system 230.

The captured image is analyzed by the video analytics system 230 todetermine visual parameters that are used to uniquely characterize theperson. In some implementations, parameters that are used tocharacterize the person include the colors of the person's various itemsof clothing, the person's estimated height and weight, the color of theperson's hair, facial features (e.g., eyeglasses, makeup, facial hair,eye color), shape (e.g., hair style, posture), carried or worn items(e.g., bulky coats, large purses or bags, jewelry), groupings (e.g., afamily moving through the store as a unit), or other characteristicsthat are used to describe the visual attributes of a person.

In the present example, the video analytics system 230 captures one ormore images of a person at the entrance door 220 on April 17 at the time14:05:00. For example, the video surveillance camera 210 captures avideo sequence of images, or one or more separate still images, of thearea by the entrance door 220. The person appears as a character in atleast one image. The video analytics system 230 analyzes the capturedimage to extract information that describes visual characteristics ofthe character. In the illustrated example, the character is determinedto have brown hair, be approximately 5′8″ tall, and wear a red jacket.

The video analytics system 230 creates a record 240 of the observation.The record 240 includes information such as the time and location of theobservation, as well as the characterizing information about thecharacter. The record 240 is stored, for example, by the archive system135 for later retrieval and analysis.

The person then moves to a second location, such as an MP3 playerdisplay 250 that is monitored by a video surveillance camera 260. Thevideo surveillance cameras 210 and 260 are positioned to capture imagesof selected locations within a retail environment. For example,locations that are of particular interest to monitor include entrances,exits, hallways leading to secluded areas, checkout aisles, or customerservice areas. Other locations of particular interest include shelves,aisles, counters, or other displays of products, including products thatare high-risk theft items such as media players, watches, cameras, DVDs,colognes, perfumes, or other such items.

When the person nears the MP3 player display 250, the video surveillancecamera 260 captures one or more images of the person. Here, the image iscaptured on April 17 at the time 14:07:00. The video analytics system230 analyzes the captured image to extract information that describesvisual characteristics of the character. In the illustrated example, thecharacter is characterized as having brown hair, having a height ofapproximately 5′8″, and wearing a red jacket. The video analytics system230 creates a record 270 of the observation of the character. The record270 includes information such as the time and location of theobservation, as well as the characterizing information about thecharacter. The record 270 is then stored for later retrieval andanalysis.

The video surveillance system 200 includes one or more image capturedevices. In some implementations, multiple cameras are placed atrespective different locations, for example, as the video surveillancecameras 210 and 260 above. As another example, a single image capturedevice can be used to cover two or more separate areas, for example, bya relocation, pan, tilt, and/or roll of the device between shots.

The stored records 240 and 270 are analyzed, for example, by the videoanalytics system 230 to determine if one person appears at two or morelocations within the store. For example, all records created by thevideo analytics system 230 are analyzed to find similarities between twoor more of the records. Here, records 240 and 270 both indicate acharacter standing approximately 5′8″ tall, having brown hair, andwearing a red jacket. Based on this, and optionally one or more otherfactors, the video analytics system 230 determines that the first andsecond detected characters correspond to one person. That is, therecords 240 and 270 are matched with each other to the exclusion ofmultiple other records which do not describe the correspondingcharacteristics.

The stored records 240 and 270 are then analyzed to determine how longthe person has taken to move between various locations in the store. Forexample, the records 240 and 270 are compared to determine that theperson took two minutes and fifteen seconds (the difference between14:07:15 and 14:05:00) to travel from the entrance door 220 to the MP3player display 250.

The time the person has taken to move between the entrance door 220 andthe MP3 player display 250 is compared against one or more predeterminedtime limits. For example, a time criterion of less than three minuteshas previously been assigned to the entrance door 220 and the MP3 playerdisplay 250. That is, a person at the entrance door 220 who less thanthree minutes later appears at the MP3 player display 250 meets theabove time criterion. This is an example of an upper time limit: thecriterion is met whenever the detected time difference is less than theupper time limit. Similarly, in some implementations, the criterion alsoor instead includes a lower time limit. That is, the time criterion ismet if a person observed at a first location does not appear at a secondlocation until after a time longer than the lower time limit. Inexamples wherein a time criterion is met, the video analytics system 230notifies users (e.g., security guards, store associates) of the person'slocation, visible attributes, and/or suspected pattern of behavior. Forexample, detecting unusually fast or slow movements helps identifypersons whose behavior does not conform with what is expected.Appropriate measures are taken upon detection, such as dispatching anemployee to assist a customer in need of help, or alerting the police orother security personnel.

In some implementations, the notification is generated in real time(e.g., virtually instantly when the time criterion is met). For example,this provides the advantage that protective or investigative measuresare initiated essentially at the same time as, or even before, theprohibited behavior. Some implementations use the detection of a mettime criterion to flag records for future investigation. For example,the system analyzes many images stored in a repository and registersthose instances where a person's travel between monitored locationsmeets at least one time criterion.

FIGS. 3A and 3B are a timeline diagram of a collection of exampleinteractions 300 among various components of an example system foranalyzing a person's movement. In some implementations, the interactions300 are carried out by one or more of the systems 100 and 200.

The interactions 300 begin when a security video system 302 captures(312) an image at a first location and transmits (314) the capturedimage to a video analytics system 306. The video analytics system 306receives the transmitted image and analyzes it to detect whether acharacter is present in the image. If a character is present in theimage, the video analytics system 306 recognizes (316) the character andidentifies characterizing attributes of the character, such as thecharacter's approximate height, weight, color of clothing, or otherattributes that are used to recognize a person represented by thecharacter. The characterizing attributes of the character are associatedwith information identifying the location and time at which thecharacter was recognized.

The video analytics system 306 transmits (318) a collection of timestamped identifying characteristics and location information to anarchive system 308. The archive system 308 stores (320) the time stampedidentifying characteristics and location information, for example, in adatabase or other information storage repository.

After the first image is captured (312), a security video system 304captures (322) an image at a second location and transmits (324) thecaptured image to the video analytics system 306. The video analyticssystem 306 receives the transmitted (324) image and analyzes it torecognize if a character is present in the image. If a character ispresent in the image, the video analytics system 306 recognizes (326)the character, identifies characterizing attributes of the character,and associates the attributes with information identifying the locationand time at which the character was recognized.

The video analytics system 306 transmits (328) a second collection oftime stamped identifying characteristics and location information to thearchive system 308. The archive system 308 stores (330) the time stampedidentifying characteristics and location information.

Referring now to FIG. 3B, the video analytics system 306 transmits (332)a request for collection of character locations, times, and attributesto the archive system 308. The archive system 308 responds to therequest by retrieving (334) a collection of character locations, times,and attributes, and replies by transmitting (336) the collection to thevideo analytics system 306.

The video analytics system 306 processes (338) the collection ofcharacter locations, times, and attributes. In some implementations, theprocessing includes identifying one or more unique characters in thecollection. The processing by the video analytics system 306 analyzespairs of locations at which an individual person has been identified anduses the associated time stamp information to determine the lengths oftime the person has taken to travel between the pairs of locations. Thevideo analytics system 306 compares the lengths of time against one ormore predetermined criterion lengths of time that are associated withvarious pairs of locations.

The video analytics system 306 then determines (340) that a timeinterval between the two identified locations meets a time criterion. Ifa person is determined to have taken less or more than the pre-specifiedamount of time to move between monitored locations, then the timecriterion is met.

When the video analytics system determines (340) that a time criterionis met, the video analytics system 306 transmits (342) to a notificationsystem 310 a request to generate a notification about the person'sbehavior. In some implementations, the notification system includes acomputer display, a wireless device (e.g., a pager, PDA, cellulartelephone, portable price scanner), a public address system, or anothersystem for generating notifications to sales associates or securityofficers. Upon receipt of the request, the notification system 310generates (344) a notification about the detected behavior. Thenotification is generated to one or more recipients.

FIG. 4 is a flowchart of a representative process 400 for analyzing aperson's movement. In some implementations, the process 400 is carriedout by the video analytics systems 115, 230, or 306. The process 400starts when an image is received (410). For example, the security videosystem 105 captures and digitizes an image of a location and transmitsthat image to the video analytics system 115 for processing.

If a character is not detected (415) within the image, then the process400 readies itself to receive (410) another image. Otherwise, if animage of a person is recognized (415) within the image, then the imageis analyzed (420) to characterize the recognized character.

A time-stamped record of the character's location and characteristicsare stored (425) in a database. In some implementations, the time-stamprepresents the time at which the image was captured or the time at whichthe image was received (410). In some implementations, the locationrecord describes the store area captured by the received (410) image ora store sub-area within the image in which the character was identified.

The database is then queried (430) for a collection of times andlocations where a character has been. For example, the database isqueried (430) for all records that include characteristics that describea 5′3″ woman with a blue coat and a red shoulder bag. In someimplementations, the character is the person recognized (415) in theimage. For example, the characteristics derived from a recently analyzed(420) image are used to trigger the query (430) of the database. In someimplementations, the database is queried (430) for a collection of timeindexed locations where any character has been identified and thatcollection is then sorted and/or filtered to determine one or moresub-collections wherein each sub-collection corresponds to times andlocations in which an individual character has been identified.

In some implementations, the query (430) is limited to return only thoserecords which were time-stamped within a predetermined timeframe. In anexample wherein several different people having similar physicalappearances have visited a retail store over a period of days or weeks,the query is modified to reduce misidentification of persons byrequesting only those records that have been stored since the storeopened for business that same day, or within the last day, hour, orother period of time. In another example, the query is limited to onlythose collections of records that describe the locations at which thesame character has been identified but do not include an entry thatidentifies the person exiting the space (e.g., the query only returnsinformation about persons who are probably still within the building).

In some implementations, a combination of exit records and time limitsis used. For example, the query is limited to assume that peoplegenerally do not stay in the space for more than two hours and thereforeignores records for a person who has not been seen in more than twohours even if the person has not been identified while exiting the space(e.g., the person was obscured from the exit camera's view while theperson was leaving).

If the same character is determined (435) to have not been previouslyidentified at other locations, then the process 400 readies itself toreceive (410) another image. For example, the image of the identifiedcharacter was captured as the person first walked through an entrance,possibly making this the first and only record of the person in thedatabase. However, if the same character is determined (435) to havebeen previously identified at other locations, then a next previouslocation is selected (440) from the collection. For example, thecharacteristics stored along with time and location information for tworecords in the database is compared, and if the characteristics used todescribe the person identified in both locations have a strongcorrelation (e.g., the two sets of independently analyzedcharacteristics are 90% similar), then the first character and thesecond character are determined (435) to correspond to one person.

If a predetermined time criterion has not been defined for the lastlocation and the selected location, then an additional determination(465) is made whether another previous location exists in thecollection. For example, various pairs of locations are associated withpredetermined time limits that describe criteria that are used togenerate notifications, as will be described below. Otherwise, if apredetermined time criterion is determined (445) to exist between thelast location and the selected location, then a time differential iscalculated (450) between the character's appearance at the last locationand the selected location.

If the time differential does not meet a predetermined time criterion(455), then an additional determination (465) is made whether anotherprevious location exists in the collection. However, if the timedifferential meets the time criterion (455), then a notification isgenerated (460). In some implementations, the notification (460)includes information that describes the recognized character, such aswhen and where the character was last seen, as well as the analyzed(420) characteristics of the character, to help store personnel locateand visually identify the person in question. For example, one or morecaptured images are distributed.

If another previous location is determined (465) to exist in thecollection, then it is selected (440) for additional processing.Otherwise, if no previous location exists, then the process 400 ends. Insome implementations, the process 400 repeats by returning to receive(410) another image.

As described previously, the length of time a person takes to movebetween two locations is used to decide whether to generate anotification of store personnel, or take other action. In otherimplementations, a person's movement among several locations is analyzedto determine whether the person's actions are indicative of an eventthat warrants further attention from store personnel, for example, aswill now be described.

FIG. 5 illustrates an example of a person's movement 500 aong severallocations. For example, using techniques described herein, a person isidentified at an entrance location 510, then at a clothing displaylocation 520, then at a jewelry display location 530, then at anelectronics display location 540, then at a video camera display 550 andthen at an exit location 560. In some implementations, store employeesare notified if the person is identified as having moved quickly among anumber of locations. For example, various locations in a store areassociated with various weighting factors (e.g., displays that arelikely targets for shoplifting are given different weighing values thanother displays). A combined score of the totalized weighing values ofvisited locations is generated and associated with the characterrepresenting the person. In a sense, the combined score indicateswhether the person has visited a variety of areas in the store orpredominantly high-risk areas. When a person appears at a location, suchas the exit 560, with an exceptionally high combined score, anotification is generated. Any technique for combining weighting factorsis used, for example adding or multiplying individual factors.

In some implementations, the combined score is adjusted one or moretimes. For example, the value of each weighting factor or thecharacter's combined score is reduced based on the time it takes theperson to move between locations. For example, this allows the system todistinguish between a person who leisurely browses a number ofhigh-value displays from a person who moves rapidly among a number oflocations before appearing at a location such as the exit location 560.As another example, the combined score (or individual weights) isreduced when the person appears at certain locations. For example,compare the situation of one person visiting high-risk areas of a storeand then appearing at the exit door without visiting the cash registerwith that of another visitor of the high-risk areas who passes throughthe cash register line before heading for the exit door. The latterperson accumulates a high combined score by visiting the high-risk areasbut the score is reduced at checkout. The former person, in contrast,still has the high combined score at the exit door which, in somesituations, triggers a notification.

In some implementations, the time a person spends in various locationsis considered when calculating the amount of time a person has taken tomove between locations. For example, time spent in checkout lanes, guestservices areas, and restrooms are credited or ignored when determiningwhether a person meets a specific time criterion. In an example, a timelimit of 30 minutes is associated with the entrance location 510 and theexit location 560, wherein a person meets the time criterion if theperson takes longer than 30 minutes to exit the store after entering.However, if the person is identified as having spent ten minutes in acustomer service line and another five minutes waiting to check out,then fifteen minutes are deducted from the person's total time.

Although exemplary embodiments have been described with reference to thefigures, other implementations are possible.

FIG. 6 is a schematic diagram of an example of a generic computer system600. The system 600 can be used for the operations described inassociation with the method 300 according to one implementation. Forexample, the system 600 may be included in either or all of the videoanalytics systems 115, 230, 306, the security video system 105, thearchive systems 135, 308, the computer 140, the handheld device 145, andthe notification system 310.

The system 600 includes a processor 610, a memory 620, a storage device630, and an input/output device 640. Each of the components 610, 620,630, and 640 are interconnected using a system bus 650. The processor610 is capable of processing instructions for execution within thesystem 600. In one implementation, the processor 610 is asingle-threaded processor. In another implementation, the processor 610is a multi-threaded processor. The processor 610 is capable ofprocessing instructions stored in the memory 620 or on the storagedevice 630 to display graphical information for a user interface on theinput/output device 640.

The memory 620 stores information within the system 600. In oneimplementation, the memory 620 is a computer-readable medium. In oneimplementation, the memory 620 is a volatile memory unit. In anotherimplementation, the memory 620 is a non-volatile memory unit.

The storage device 630 is capable of providing mass storage for thesystem 600. In one implementation, the storage device 630 is acomputer-readable medium. In various different implementations, thestorage device 630 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 640 provides input/output operations for thesystem 600. In one implementation, the input/output device 640 includesa keyboard and/or pointing device. In another implementation, theinput/output device 640 includes a display unit for displaying graphicaluser interfaces.

The features described can be implemented in digital electroniccircuitry or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device or in a propagated signal, for executionby a programmable processor; and method steps can be performed by aprogrammable processor executing a program of instructions to performfunctions of the described implementations by operating on input dataand generating output. The described features can be implementedadvantageously in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system, at least one input device, andat least one output device. A computer program is a set of instructionsthat can be used, directly or indirectly, in a computer to perform acertain activity or bring about a certain result. A computer program canbe written in any form of programming language, including compiled orinterpreted languages and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory including, by way ofexample, semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope. For example, advantageous results may beachieved if the steps of the disclosed techniques were performed in adifferent sequence, if components in the disclosed systems were combinedin a different manner, or if the components were replaced orsupplemented by other components. The functions and processes (includingalgorithms) may be performed in hardware, software, or a combinationthereof, and some implementations may be performed on modules orhardware not identical to those described. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A method for analyzing a person's movement in aretail environment, comprising: capturing a first image of a firstperson at a first location at a first time; capturing a second image ofa second person at a second location, remote from the first location, ata second time; identifying, by analyzing with at least one computingdevice, one or more characteristics associated with the image of thefirst person and the image of the second person; determining, byanalyzing with the at least one computing device, whether the firstperson and the second person are likely to be the same individual; inresponse to a determination that the first person is likely to be thesecond person, comparing, with the at least one computing device, theelapsed time between the first time and the second time with a maximumelapsed time threshold that is indicative of a likelihood that the firstperson has committed or will commit a crime within the retailenvironment; and determining, by analyzing with the at least onecomputing device, how many of a plurality of locations in a retailenvironment the first person traveled to within a predetermined periodof time include a merchandise display that is likely a target ofshoplifting.
 2. The method of claim 1, wherein the maximum elapsed timethreshold is based at least in part on the distance between the firstlocation and second location and the nature of the retail environment.3. The method of claim 1, wherein the maximum elapsed time threshold isbased in part on an empirical assessment of data associated withprevious monitoring of the first location and the second location. 4.The method of claim 1, further comprising determining whether theelapsed time is above a minimum elapsed time threshold.
 5. The method ofclaim 4, wherein the minimum elapsed time threshold and the maximumelapsed time threshold cooperatively define an elapsed time windowindicative of an increased likelihood that the first person hascommitted or will commit a crime within the retail environment.
 6. Themethod of claim 1, wherein the one or more characteristics comprisecolor, size, shape, gender, clothing, facial feature, body feature, anitem associated with the person, or combinations thereof.
 7. The methodof claim 1, wherein the first location is proximate an entry to theretail environment.
 8. The method of claim 1, wherein the first locationis proximate a product display including items at increased risk forshoplifting.
 9. A system for analyzing a person's movement, the systemcomprising: an electronic image capture system configured to capture (i)a first image of a first scene of a first location and (ii) a secondimage of a second scene of a second location remote from the firstlocation, the first and second images being associated with first andsecond time stamps, respectively; at least one computing device havinginstructions to recognize (i) a first character in the first image and(ii) a second character in the second image, wherein first and secondparameters characterize attributes of the first and second characters,respectively; and a video analytics system having instructions that (i)determines, using at least the first and second parameters, that thefirst and second characters correspond to one actor; (ii) determineswhether a predetermined time criterion exists between the first sceneand the second scene; (iii) compares a maximum elapsed time criterionwith a time difference between the first and second time stamps only ifa predetermined time criterion exists between the first scene and thesecond scene, the maximum elapsed time criterion is indicative of thelikelihood that the first character will commit a crime; and (iv)determines how many of a plurality of locations in a retail environmentthe first character traveled to within a predetermined period of timeinclude a merchandise display that is a likely target of shoplifting.10. The system of claim 9, wherein each of the first and secondparameters includes a parameter selected from: color, size, shape,gender, clothing, facial feature, body feature, an item associated withthe actor, and combinations thereof.
 11. The system of claim 9, whereinthe video analytics system further determines whether the timedifference is greater than a minimum elapsed time threshold, therebydetermining whether the elapsed time is within an elapsed time window.12. The system claim 11, wherein the elapsed time window is based atleast in part on previous monitoring of the first and second scenesusing the electronic image capture system.
 13. The system of claim 12,wherein the previous monitoring comprises: performing observation over aperiod of time, using the electronic image capture system, of at leastpart of an area that includes the first and second scenes; andidentifying, among multiple actors in the area, a first actor whoengages in prohibited behavior during the period of time; wherein thetime limit is defined based at least in part on the observation of thefirst actor.
 14. The system claim 9, wherein both the first and secondscenes include a retail store ingress or egress.
 15. The system claim 9,wherein both the first and second scenes include a retail productdisplay.
 16. The system claim 15, wherein only one of the first andsecond scenes includes products at an increased risk of shoplifting. 17.The system claim 15, wherein the first and second scenes both includeproducts at an increased risk of shoplifting.
 18. The system of claim 9,wherein only one of the first and second scenes include a retailingress.
 19. The system of claim 9, wherein the actor is a human.
 20. Acomputer-implemented method of analyzing an individual's movements in aretail environment, comprising the steps of: operating a videosurveillance system to monitor a plurality of locations at a retailenvironment, at least some of the plurality of locations include amerchandise display that is a likely target of shoplifting; determining,by analyzing with at least one computing device, a plurality oflocations at the retail environment that a first individual travels towithin a predetermined period of time and assigning a crime score to thefirst individual based at least in part on how many of the plurality oflocations that the first individual traveled to within the predeterminedperiod of time include a merchandise display that is a likely target ofshoplifting, the at least one computing device being configured todifferentiate the first individual from other individuals at the retailenvironment; determining the elapsed time the first individual traveledfrom a first location at the retail environment to a second location ofthe retail environment, at least one of the first location and thesecond location includes a merchandise display that is a likely targetof shoplifting; adjusting the crime score of the first individual basedat least in part on the elapsed time the first individual traveled fromthe first location to the second location; and, generating a securitynotification specific to the first individual if the first individual'scrime score exceeds a predetermined value.